CSAL4243: Introduction to Machine Learning¶
Muhammad Mudassir Khan (mudasssir.khan@ucp.edu.pk)
Lecture 1: Introduction¶
Overview¶
What is Machine Learning? ¶
Need for Intelligent Machines¶
- Human interest in intelligent machines
- Replicate human functionalities
- Traditional algorithms were hard coded
Problems with hard-coded algorithms¶
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from IPython.display import Image
Image(filename='./images/challenges.jpeg', width=500)
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Learning algorithms¶
- How human learns?
- From observation/examples
- Trial and error
- Practice for improving performance
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from IPython.display import YouTubeVideo
YouTubeVideo("TeFF9wXiFfs")
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YouTubeVideo("M5pj2CrO-2w")
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- Machine Learning is making computers/machcines learn from data
- Learning improve over time with more data
Definition¶
Mitchell ( 1997 ) define Machine Learning as “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P , if its performance at tasks in T , as measured by P , improves with experience E .”
Example: playing checkers.
T = the task of playing checkers.
E = the experience of playing many games of checkers
P = the probability that the program will win the next game.
Examples¶
- Emails -----> spam / no-spam ?
- Image -----> human ?
- Audio -----> Text
- English -----> Urdu
- Text -----> Audio
- Ad,user -----> click / not click ?
- Self Driving Cars
The three different types of machine learning¶
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Image(filename='./images/01_01.png', width=500)
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Learning from labled data with supervised learning¶
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Image(filename='./images/01_02.png', width=500)
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Examples: ¶
- Spam or non-spam email
- cat or dog from a picture
- disease or no-disease from test results
- positive or negative customer review
- predict hourse price from size
- stock rates
Regression for predicting continuous outcomes¶¶
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Image(filename='./images/01_04.png', width=300)
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Image(filename='./images/01_11.png', width=500)
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Classification for predicting class labels¶
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Image(filename='./images/01_03.png', width=300)
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Image(filename='./images/01_12.png', width=600)
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Discovering hidden structures with unsupervised learning¶
- Finding patterns in unlabled data
Finding subgroups with clustering¶
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Image(filename='./images/01_06.png', width=300)
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Solving interactive problems with reinforcement learning¶
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Image(filename='./images/01_05.png', width=300)
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An introduction to the basic terminology and notations¶
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Image(filename='./images/01_08.png', width=450)
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A roadmap for building machine learning systems¶
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Image(filename='./images/01_09.png', width=500)
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Would you have survived Titanic?¶
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from __future__ import print_function
import numpy as np
import tflearn
# Download the Titanic dataset
from tflearn.datasets import titanic
titanic.download_dataset('titanic_dataset.csv')
# Load CSV file, indicate that the first column represents labels
from tflearn.data_utils import load_csv
data, labels = load_csv('titanic_dataset.csv', target_column=0,
categorical_labels=True, n_classes=2)
# Preprocessing function
def preprocess(data, columns_to_ignore):
# Sort by descending id and delete columns
for id in sorted(columns_to_ignore, reverse=True):
[r.pop(id) for r in data]
for i in range(len(data)):
# Converting 'sex' field to float (id is 1 after removing labels column)
data[i][1] = 1. if data[i][1] == 'female' else 0.
return np.array(data, dtype=np.float32)
# Ignore 'name' and 'ticket' columns (id 1 & 6 of data array)
to_ignore=[1, 6]
# Preprocess data
data = preprocess(data, to_ignore)
print (data)
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# Build neural network
net = tflearn.input_data(shape=[None, 6])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net)
# Define model
model = tflearn.DNN(net)
# Start training (apply gradient descent algorithm)
model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)
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# Let's create some data for DiCaprio and Winslet
dicaprio = [3, 'Jack Dawson', 'male', 19, 0, 0, 'N/A', 5.0000]
winslet = [1, 'Rose DeWitt Bukater', 'female', 17, 1, 2, 'N/A', 100.0000]
user = [2, 'user', 'female', 20, 0, 2, 'N/A', 50.0000]
# Preprocess data
dicaprio, winslet, user = preprocess([dicaprio, winslet, user], to_ignore)
# Predict surviving chances (class 1 results)
pred = model.predict([dicaprio, winslet, user])
print("DiCaprio Surviving Rate:", pred[0][1])
print("Winslet Surviving Rate:", pred[1][1])
print("user Surviving Rate:", pred[2][1])
Applications¶
- ## Algrithm that plays Atari breakout
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from IPython.display import YouTubeVideo
YouTubeVideo("V1eYniJ0Rnk")
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- ## Machine Learning algorithm is better than human at object recognition
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from datetime import timedelta
start=int(timedelta(hours=0, minutes=7, seconds=47).total_seconds())
YouTubeVideo("BfDQNrVphLQ", start=start, autoplay=1, theme="light", color="red")
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- ## Scene understanding
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Image(filename='./images/01_10.png', width=500)
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- ## AlphaGo beat Human GO expert
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YouTubeVideo("PQCrX1sQSzY")
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- ## Self Driving Cars
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YouTubeVideo("MqUbdd7ae54")
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Music Generation
Art Generation (Mario levels)
Story Writing
Speech recognition (personal assistants, chat bots)
Face recognition
Competitions¶
Lung cancer detection --- prize $1,000,000
Youtube 8m video tagging --- prize $100,000
Detect and classify fish --- prize $150,000
IMAGENET (Large Scale Visual Recognition) --- Fame
Efficient ConvNets for Semantic Segmentation --- prize undeclared
Titanic survival prediction --- Fame and ranking
Summary¶
Machine learning is at the heart of all technologies today.
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start=int(timedelta(hours=0, minutes=1, seconds=23).total_seconds())
YouTubeVideo("jBLN1UJbiyw", start=start, autoplay=1, theme="light", color="red")
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Credits¶
Raschka, Sebastian. Python machine learning. Birmingham, UK: Packt Publishing, 2015. Print.